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Abstract

Information Filtering is concerned with filtering data streams in such a way as to leave only pertinent data (information) to be perused. when the data streams are produced in a changing environment the filtering has to adapt too in order to remain effective. Adaptive Information Filtering (AIF) is concerned with filtering in changing environments. the changes may occur both on the transmission side (the nature of the streams can change), and on the reception side (the interest of a user can change). Weighted trigram analysis is a quick and flexible technique for describing the contents of a document. a novel application of evolutionary computation is its use in Adaptive Information Filtering for optimizing various parameters, notably the weights associated with trigrams. the research described in this paper combines weighted trigram analysis, clustering, and a special two-pool evolutionary algorithm, to create an Adaptive Information Filtering system with such useful properties as domain independence, spelling error insensitivity, adaptability, and optimal use of user feedback while minimizing the amount of user feedback required to function properly. We designed a special evolutionary algorithm with a two-pool strategy for this changing environment.